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Article << Previous     |     Next >>   Contents Vol 60(2)

Is sediment mud content a significant predictor of macrobenthos abundance in low-mud-content tidal flats?

T. Sakamaki A B C, O. Nishimura A

A Department of Civil Engineering, Tohoku University, 6-6-06 Aramaki-Aza-Aoba, Sendai 980-8579, Japan.
B Present address: Transdisciplinary Research Organization for Subtropics and Island Studies, University of the Ryukyus, 1 Senbaru, Nishihara, Okinawa 903-0213, Japan.
C Corresponding author. Email: takashi.sakamaki@ubc.ca
 
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Abstract

Sediment mud content is a significant predictor of macrobenthos abundance in marine soft-bottom habitats. However, the mechanisms behind the non-linearity generally shown in mud–macrobenthos relationships have not been examined. The present study analysed spatial distributions of sediment properties and macrobenthos in a tidal flat system of the north-east coast of Japan and tested the hypothesis: the non-linearity of mud–macrobenthos relationship corresponds to changes in bed elevation variability and sediment cohesiveness. For two macrobenthos species studied, Nuttallia olivacea (Bivalvia) and Hediste spp. (Polychaeta), the relationships between mud content (<75 μm) and their density showed discontinuities at ~0.1% and ~20% mud contents. In stations with <0.1% mud content, the densities of both species were related positively to mud content and negatively to bed elevation variability, suggesting that mud content reflected negative effects of bed elevation variability on macrobenthos abundance. The stations where distinct changes were observed in macrobenthos densities below or above ~20% mud content were within an area where a shift between non-cohesive and cohesive sediments was found. Hence, the results of the present study support the hypothesis and also suggest that sediment mud content can be a sensitive predictor of macrobenthos abundance even within a relatively low and narrow range of mud content.

Keywords: benthic community, marine soft-bottom habitats, Nanakita River estuary, regression model, spatial distribution.


   
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